Method and device for automatic identification of measurement item and ultrasound imaging apparatus

ABSTRACT

A method for automatic identification of a measurement item is provided. The method comprises acquiring, via an image acquisition module, gray values of pixels of a specified section image corresponding to ultrasonic echoes generated by reflection of ultrasound waves by a tissue under examination; identifying, via an identification module, at least one measurement item corresponding to the specified section image based on the gray values of the pixels; and measuring, via a measuring module, a measurement item parameter of the specified section image based on the measurement item identified. Because the measurement item of a specified section image can be automatically identified based on the content thereof, the user does not need to move a trackball to select measurement items, and therefore efficiency is increased.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/271,095, filed Sep. 20, 2016, for “METHOD AND DEVICE FOR AUTOMATICIDENTIFICATION OF MEASUREMENT ITEM AND ULTRASOUND IMAGING APPARATUS,”which is a continuation of PCT Application No. PCT/CN2014/073777, filedMar. 30, 2014, both of which are incorporated herein by reference.

TECHNICAL FIELD

The present disclosure relates to medical equipment, in particular to anultrasound imaging apparatus and a method and device for automaticidentification of a measurement item thereof.

BACKGROUND

Ultrasound imaging apparatus are generally used by a doctor to inspecttissues inside a human body. The doctor can place an ultrasound probeonto the surface of the skin corresponding to a tissue to obtainultrasound images of the tissue. Due to its characteristics of safety,convenience, non-invasion and low cost, etc., ultrasound imaging hasbecome a main assisting means for diagnosis.

In order to obtain measurements of an object of interest, the doctorneeds to perform many measuring operations during the ultrasoundexamination. Since there are generally a plurality of measurement itemswhich need to be measured for a tissue being examined in one measuringmode, while the measuring is a process in which the user needs toconstantly interact with the apparatus, the user needs to constantlyselect the measurement item and then move a trackball to perform themeasuring, which is time-consuming. For example, in an abdomen mode, thecommon measurement items include the size of the liver, gallbladder,spleen and kidneys, etc. In an obstetric mode, head circumference (HC),biparietal diameter (BPD), abdominal circumference (AC) and femur length(FL) are necessary measurement items for each examination. Generally,during an examination, the doctor may obtain a standard section imageand then press a button for measuring, causing a measurement menu to bedisplayed by the apparatus. Next, the doctor may move the trackball toselect a desired measurement item in the menu, and then the measuring ofthe selected measurement item can be performed. For example, in anobstetric examination, after obtaining a corresponding standard sectionimage, the doctor first presses the button for measuring to open themeasurement menu, and then moves the trackball to select a desiredmeasurement item in the menu. Taking the head circumference as anexample, the doctor first rotates the trackball to move the cursor tothe measurement menu, and selects the measurement item of headcircumference in the menu. After the measurement item is selected, thedoctor rotates the trackball to move the cursor to a position at oneside of the skull ring in the section image and presses a button forconfirmation to determine a first point, and then moves the cursor tothe other side and presses the button for confirmation to determine asecond point, thereby obtaining one axis of an ellipse. Then, the doctorcan move the cursor to adjust the length of the other axis of theellipse and probably adjust the two points determined, until the ellipsematches the skull of the fetus. Therefore, in one measuring, many pointsneed to be determined in order to match the ellipse with the structureto be measured. In the case where the object to be measured is linear,at least two points need to be determined. It was reported that a doctorwill spend 20% to 30% of the time on measuring.

Certain methods for automatic measurement have been proposed in somepatents or publications in order to save the measuring time of thedoctor. However, in these methods, the user needs to manually select thecorresponding measurement item in the menu according to the obtainedsection image and then perform an automatic or semi-automatic measuring,which will directly affect the degree of automation of the automaticmeasuring. Furthermore, the operations of the doctor on the measurementitem will affect the measuring time. Moreover, the doctor usually doesnot like the distraction of constantly pressing the button and selectingitems in the menu during the examination.

SUMMARY

According to an aspect of the present disclosure, a method for automaticidentification of a measurement item is provided. The method mayinclude:

acquiring gray values of pixels of a specified section image, whereinthe gray values of the pixels correspond to ultrasound echoes generatedby reflection of ultrasound waves by a tissue under an examination;

identifying at least one measurement item corresponding to the specifiedsection image based on the gray values of the pixels; and

measuring a measurement parameter of the specified section image basedon the measurement item identified.

According to another aspect of the present disclosure, an ultrasoundimaging apparatus is provided. The apparatus may include a probe whichtransmits ultrasound waves to a tissue and receives ultrasound echoes, asignal processor which processes the ultrasound echoes to generateultrasound image data, and an image processor which processes theultrasound image data and generates section images. The image processormay be further configured to:

acquire gray values of pixels of a specified section image, wherein thegray values of the pixels correspond to ultrasound echoes generated byreflection of ultrasound waves by a tissue under an examination;

identify at least one measurement item corresponding to the specifiedsection image based on the gray values of the pixels; and

measure a measurement parameter of the specified section image based onthe measurement item identified.

Based on the method/device for automatic identification of a measurementitem provided by the present disclosure, the measurement items of aspecified section image can be automatically identified based on thecontents of the specified section image, such that the user does notneed to select the measurement items during the measurement, and therebythe measurement is more convenient and automatic.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 schematically shows an ultrasound imaging apparatus according toan embodiment of the present disclosure;

FIG. 2 schematically shows a device for automatic identification of ameasurement item according to an embodiment of the present disclosure;

FIG. 3 is a flow chart of a method for automatic identification of ameasurement item according to an embodiment of the present disclosure;

FIG. 4 schematically shows an identification module provided byembodiment 1 of the present disclosure;

FIG. 5 is a flow chart of an identification method provided byembodiment 1 of the present disclosure;

FIG. 6 schematically shows an identification module provided byembodiment 2 of the present disclosure;

FIG. 7 is a flow chart of an identification method provided byembodiment 2 of the present disclosure; and

FIGS. 8 a-c schematically show the measurement item section in theobstetric measuring mode according to embodiment 2, where FIG. 8 aschematically shows the head circumference section, FIG. 8 bschematically shows the abdominal circumference section, and FIG. 8 cschematically shows the femur section.

DETAILED DESCRIPTION

Medical ultrasound imaging is generally used by doctors to inspecttissues inside the human body. The doctor can place an ultrasound probeonto the surface of the skin to obtain ultrasound section images of thetissue below the skin. Referring to FIG. 1 , which shows the structureof an ultrasound imaging apparatus, the ultrasound imaging apparatus mayinclude an ultrasound wave generation circuit 11, a probe 1, a signalprocessor 2, an image processor 3 and a display 4.

The probe 1 may transmit ultrasound waves to a scanning object andreceive the ultrasound echoes. The ultrasound wave generation circuit 11may generate waveform data and excite transducers of the probe 1 by atransmit channel 12 to transmit ultrasound waves to the tissue to beexamined. After the reflection and absorption of the ultrasound waves,the ultrasound echoes can be formed. The probe 1 may receive theultrasound echoes and output them to the signal processor 2 through areceive channel 13.

The signal processor 2 may process the ultrasound echoes to generateultrasound image data. The signal processor 2 may perform beam-formingon the ultrasound echoes received through the receive channel 13 toobtain radio frequency signals, and then perform quadrature demodulationon the radio frequency signals to obtain quadrature demodulated basebandsignals. The processed ultrasound image data may be output to the imageprocessor 3.

The image processor 3 may process the ultrasound image data and generatethe section images, and send the section images to the display 4 fordisplaying. The image processor 3 may include a device for automaticidentification of measurement items, which may process the ultrasoundimage data output by the signal processor 2 to identify the measurementitems corresponding to the section image specified by the user, andfurther perform the measuring of the measurement parameterscorresponding to the measurement items.

The display 4 may display the section images generated by the imageprocessor 3 and the measurement parameters.

FIG. 2 schematically shows the structure of the device for automaticidentification of measurement items, which may include an imageacquisition module 31, an identification module 32 and a measuringmodule 33.

The image acquisition module 31 may acquire the gray value of the pixelsin the specified section image. The gray value of the pixels maycorrespond to the ultrasound echoes generated by the reflection of theultrasound waves by the tissue being examined.

The identification module 32 may identify at least one measurement itemcorresponding to the specified section image based on the gray value ofthe pixels. For example, the identification module 32 may process thegray values of the pixels and perform a comparative analysis with apreset data model, and identify the measurement item according to theresult of the analysis.

The measuring module 33 may perform measuring on the specified sectionimage to obtain the measurement parameters based on the identifiedmeasurement item.

In another embodiment, the device for automatic identification ofmeasurement items may further include a measuring mode acquisitionmodule 34, which may acquire the measuring mode selected by the user.The identification module 32 may identify the measurement itemscorresponding to the specified section image according to the measuringmode selected by the user during the identification of the measurementitems.

A flow chart of a method for automatic identification of measurementitems using the ultrasound imaging apparatus described above is shown inFIG. 3 . The method may include following steps.

Step 400: detecting a measurement instruction input by the user. Themeasurement instruction may be generated by the user by pressing abutton for measuring or selecting a measurement item. When a measurementinstruction is detected, the following steps may be performed.

Step 410: acquiring the image. The image processor may send theprocessed ultrasound image data to the display, where the processedultrasound image data may be displayed. The user may specify a sectionimage by observing the displayed images. According to the section imagespecified by the user, the gray values of pixels in the specifiedsection image may be acquired from stored image data. The specifiedsection image may generally be the section image which is presentlydisplayed. The gray value of the pixels may correspond to the ultrasoundechoes formed by the reflection of the ultrasound signals by the tissue.The ultrasound echoes of bones are strong, and the corresponding imagehas a great gray value. The ultrasound echoes of soft tissue are weak,and the corresponding image has a small gray value.

Step 420: identifying the measurement item. In one measuring mode, forthe tissue being examined, there may generally be a plurality ofmeasurement items which need to be measured. For example, in an abdomenmeasuring mode, the common measurement items may include the size of theliver, gallbladder, spleen and kidneys, etc. In an obstetric measuringmode, the common measurement items may include head circumference (HC),biparietal diameter (BPD), abdominal circumference (AC) and femur length(FL), etc. In other embodiments, there may be other measuring modes, andthus there may correspondingly be other corresponding measurement items.The measurement items may be determined according to the section imageof the object to be measured. There are always differences between thesection images corresponding to different measurement items, and thesedifferences make the automatic identification possible. One possiblesolution is presetting data models of section images corresponding tothe plurality of measurement items in the apparatus and identifying themeasurement item by comparing the gray values of pixels in the specifiedsection image with the preset data models. The preset data models may bethe characteristics which are able to distinguish a certain sectionimage from other section images, such as a training sample model. Thepreset data models may also be the physical characteristics (such as theshape, the brightness range or the size, etc.) of the sectionscorresponding to the measurement items. When the section image of ameasurement object is determined, the measurement items for thismeasurement object are determined. On the contrary, when the measurementitems are determined, the section image of the measurement objectcorresponding to these measurement items needs to be displayed. Forexample, for the obstetric examination, the head circumference and theabdominal circumference are elliptic objects, the biparietal diameterand the femur length are linear object, and the head circumference andthe biparietal diameter can be measured in a same section. Therefore,these four measurement items may correspond to three measurementsections: the head circumference section (HC and BPD may correspond to asame section), the abdominal circumference section and the femursection. When the section image specified by the doctor is an abdominalcircumference section, the measurement item may be the abdominalcircumference (AC); when the specified section image is a femur section,the measurement item may be the femur length (FL); and when the sectionimage specified by the doctor is the head circumference section, themeasurement items may be the head circumference (HC) and/or thebiparietal diameter (BPD). Accordingly, in the present embodiment, theimage processor may process and analyze the gray values of the pixels toidentify the type of the current specified section image, and therebyidentify the measurement items corresponding to this specified sectionimage.

Step 430: measuring the parameters. Based on the measurement itemsidentified in the step 420, the parameters corresponding to themeasurement items of the specified section image may be measured. In theactual measurement process, the parameters may be measured manually,semi-automatically or automatically. When a certain section imagecorresponds to two or more measurement items (for example, themeasurement items of the head circumference section may include the headcircumference (HC) and the biparietal diameter (BPD)), the measurementitems identified may be measured one by one.

In other embodiments, the method may further include the following stepsbefore identifying the measurement items in the step 420.

Step 440: acquiring the measuring mode adopted during the examination ofthe tissue. In this step, the measuring mode selected by the user may beacquired, which can reduce the identification range of the measurementitems, and thereby not only may the identification efficiency beincreased, but the identification accuracy may also be improved.

In the embodiments of the present disclosure, the measurement items canbe identified automatically based on the images, such that the doctorneed not select the measurement item in the menu by moving thetrackball, and thereby the efficiency of the measuring can be increased.

In the embodiments of the present disclosure, solutions for automaticidentification of the measurement items may be added. These solutionsmay be implemented in a plurality of ways that will be further describedin detail with reference to specific embodiments.

Embodiment 1

Differences exist between sections corresponding to differentmeasurement items. Based on this fact, in the present embodiment,characteristics which can indicate the section corresponding to certainmeasurement items may be extracted, and the measurement items may beidentified based on the characteristics. In one embodiment, thecharacteristics of the sections may be extracted and classified using amachine learning method. Referring to FIG. 4 , an identification moduleof the present embodiment is shown, which may include a characteristicgeneration unit 3211, a comparison unit 3212 and a searching unit 3213.

The characteristic unit 3211 may generate the characteristics of thespecified section image using the gray values of the pixels in thespecified section image. The comparison unit 3212 may compare thecharacteristics of the specified section image with the characteristicsof training samples of a preset training sample model. The searchingunit 3213 may search the training sample whose characteristics are mostsimilar to the characteristics of the specified section image, andoutput the measurement items corresponding to the training samplesearched as the corresponding measurement items corresponding to thespecified section image.

Based on the identification module above, the present embodiment furtherprovides a machine learning identification method. The machine learningmethod generally generates the characteristics of a plurality of samplesby the information of training samples (a series of samples for whichthe measurement items are known) and compares the characteristics of thesection sample to be measured with the characteristics of the trainingsamples to determine which type of measurement items the section sampleto be measured corresponds to. In the art, common machine learningmethods may include principal component analysis (PCA) method, lineardiscriminant analysis (LDA) method, kernel principal component analysis(KPCA) method, locality preserving projections (LPP), support vectormachine (SVM) method and artificial neural networks (ANNs), etc.

Generally, the dimensionality of the image data acquired by the imageacquisition module is very high. An image with sizes of W*H may beconsidered as a W*H-dimensional vector. A great relevance generallyexists between the dimensions of this high-dimensional vector. In otherwords, the expression of high-dimensional data has great redundancy. Onecommon method is projecting the high-dimensional data to alow-dimensional space to eliminate the redundancy between the dimensionsof the data. PCA is this kind of method, of which the essence is findingout the projection which best represents the original high-dimensionaldata in the sense of least mean square. The present embodiment will befurther described taking PCA as an example. However, a person ordinarilyskilled in the art will understand that, in other embodiments, thetechnical solutions of the present embodiment may also be implementedusing other machine learning methods (such as LDA, KPCA, LPP, SVM, orANNs, etc.) according to the concept of the present embodiment withoutcreative work.

Referring to FIG. 5 , a method for identification using theidentification module above may include following steps.

Step 510: generating the characteristics. In this step, thecharacteristics of the specified section image may be generated usingthe gray values of the pixels of the specified section image.

In the present embodiment, the measurement items of the specifiedsection image may be obtained by comparing the characteristics of thespecified section image with the characteristics of the preset trainingsamples. Therefore, first, the characteristics of the training samplesand the characteristics of the specified section image may be acquired.In an embodiment, the characteristics of the training samples and thecharacteristics of the specified section image may be the eigenvalues,the eigenvectors or a combination of the eigenvalues and theeigenvectors. In an embodiment, the characteristic of the trainingsample may be the projection coefficient of the eigenvectors of thetraining sample on the mean value of the training sample, and thecharacteristic of the specified section image may be the projectioncoefficient of the eigenvectors of the specified section image on themean value of the training sample. The advantage of this embodiment isthat the high-dimensional data is projected to the low-dimensional spaceand the redundancy between the dimensions of the data is eliminated,such that the calculation is reduced and the computational efficiency isincreased.

For a training sample library, assuming that this training samplelibrary includes N training samples, the image of each training samplehas a resolution of W×H, and each image is expanded into a M-dimensionallong vector (where M=W×H), the image of this training sample library canbe expressed as an M×N matrix, which can be expressed as [I₁, . . . ,I_(N)]_(M×N), where I, is a training sample vector.

First, the mean value of the sample (which is referred to as the meansample hereinafter) may be calculated:

$m = \frac{\sum\limits_{i = 1}^{N}I_{i}}{N}$

here m is the mean sample. New training samples with a mean value ofzero may be obtained by subtracting the samples in the training samplelibrary with the mean sample:L=[I ₁ −m, . . . ,I _(N) −m]

The covariance matrix of the new training samples may be:

$C = {{\sum\limits_{i = 1}^{N}{\left( {I_{i} - m} \right)\left( {I_{i} - m} \right)^{T}}} = {LL^{T}}}$

where L^(T) is the transpose of matrix L.

After the covariance matrix C of the new training samples is obtained,the eigenvalues of the matrix C may be calculated. Because the matrix Chas too-large dimensions, it is very difficult to calculate theeigenvectors of the matrix C directly. Therefore, the eigenvectors of asmall matrix R=L^(T)L may be calculated first.

Assuming that V is the eigenvector matrix of the small matrix R and Λ isthe eigenvalue matrix, then:(L ^(T) L)V=VΛ

Both sides of the equation may be multiplied by L to obtain:(LL ^(T))LV=LVΛ

Therefore, the orthogonalized eigenvector of the matrix C=LL^(T) may be:

$E = {L\; V\;\Lambda^{- \frac{1}{2}}}$

where the eigenvalue matrix Λ may be a M*M diagonal matrix and theeigenvalues may be arranged in descending order, i.e., Λ₁₁≥Λ₂₂≥ . . .≥Λ_(MM), where Λ_(jj) represents the element of the eigenvalue matrix Λat j-th column and j-th row.

Actually, most of the eigenvalues are very small, even zero. Therefore,it is possible for only larger eigenvalues and correspondingeigenvectors to remain. For example, only the first n eigenvalues, andonly the first n columns of the eigenvectors V, are remained. In otherwords, the dimensionality of the remaining eigenvectors V is N*n and nmay be determined in a plurality of ways. For example, n may be a presetconstant. Or, n may be a value which leads to

${{\sum\limits_{i = 1}^{n}\Lambda_{ii}} \geq {P{\sum\limits_{i = 1}^{M}\Lambda_{ii}}}},$where P is a percentage. For example, P=95% means that 95% of thecharacteristics of the original data are remained.

Then, the projection of the training samples on the mean sample (i.e.,the characteristics or principal component of the training samples) maybe obtained by:F _(i) =E ^(T)(I _(i) −m)  (1)

where E^(T) is the transpose matrix of the matrix E, and F_(i) is thecharacteristic of I_(i). This projection reduces the dimensionality ofthe sample from M*1 to n*1 and eliminates the correlation between thehigh-dimensional data. This n*1-dimensional data can best represent theoriginal data in the sense of least mean square.

A person ordinarily skilled in the art will understand that thecalculation of the step above to the sample library can be performedoffline, and the results of the calculation (for example, thecharacteristics F_(i) of the matrix E and the training samples) may bestored in the apparatus.

For a specified section image, the gray values of all pixels of thesection image may be acquired, which may be similarly expanded into anM-dimensional vector I_(test). The characteristic of the specifiedsection image may be calculated according to the formula (2):w=E ^(T)(I _(test) −m)  (2)

where w is the projection coefficient of the eigenvectors of the sectionimage on the mean value of the training sample (i.e., the characteristicof the section image), I_(test) is the eigenvectors of the sectionimage, m is the mean value of the training samples, E is theorthogonalized eigenvector, and E^(T) is the transpose matrix of thematrix E.

Step 520: comparing the characteristics. In this step, thecharacteristics of the specified section image may be compared with thecharacteristics of the plurality of training samples of the presettraining sample model.

The characteristics of the specified section image calculated by theformula (2) in the step 510 may be compared with the characteristics ofthe plurality of training samples of the preset training sample model asdefined by the formula (1) to obtain:x _(i) =∥w−F _(i)∥

where x_(i) is the modulus of the comparison result of thecharacteristic of the specified section image with the characteristic ofthe i-th training sample F_(i), where 1≤i≤N. In an embodiment, thecharacteristic of the specified section image may be compared with theplurality of samples in the training sample library.

Step 530: searching the measurement items. In this step, the trainingsample with characteristics most similar to the characteristics of thespecified section image may be searched. The measurement itemscorresponding to the training sample which is searched may be themeasurement items of the specified section image.

After the comparison of the characteristics of the specified sectionimage with the characteristics of the plurality of training samples ofthe preset training sample model, the searching of the measurement itemscorresponding to the specified section image may be performed using thefollowing formula:

${ind} = {{index}\left( {\min\limits_{1 \leq i \leq N}x_{i}} \right)}$

where the function index represents the serial number (i) correspondingto the minimum of x_(i), which means that the i-th training sample isthe one most similar to the sample being processed. Therefore, themeasurement items of the sample being processed may be the same as themeasurement items of the i-th training sample, which is known. Thereby,the measurement items corresponding to the specified section image aresearched out.

Further, in other embodiments, different measuring modes may correspondto different sample models which have different sample libraries.Therefore, before comparing the characteristics in the step 520, themeasuring mode selected by the user may be acquired to make a selectionon the sample libraries to reduce the number of samples included in thecomparison, which not only can increase the identification efficiency,but also may further increase the identification accuracy.

Embodiment 2

The machine learning method needs to acquire the characteristics of aplurality of training samples and then perform the comparison. Thereforeit may be necessary to acquire as many training samples as possible, andthese training samples should cover a variety of situations in position,size and shape, etc. In the case where there are not enough trainingsamples, image processing methods may be used. In the presentembodiment, image processing methods may be used to extract the imagefeatures (such as the gray values, shape or the like) of the sectionimages to determine the measurement items. Referring to FIG. 6 , anidentification module provided in the present embodiment is shown. Theidentification module may include an extraction unit 3221, anidentification unit 3222 and a determination unit 3223.

The extraction unit 3221 may extract the high intensity portion of thespecified section image based on the gray values of the pixels of thespecified section image. The identification unit 3222 may identify thehigh intensity portion based on the measuring mode to determine thesection type of the specified section image. The determination unit 3223may determine the measurement items corresponding to the specifiedsection image based on the section type determined and the measurementitems corresponding thereto.

The present embodiment also provides a method for identification ofmeasurement items using the identification module above, which mayextract the features (such as the gray values, shape or the like) of thesection image and then perform the analysis and determination usingthese features.

Since the image features in different measuring modes may be different,the image processing methods used may vary with the measuring modes. Forexample, in the obstetric measuring mode, the common measurement itemsmay include head circumference (HC), biparietal diameter (BPD),abdominal circumference (AC) and femur length (FL). The headcircumference and the abdominal circumference are elliptic objects, thebiparietal diameter and the femur length are linear objects, and thehead circumference and the biparietal diameter can be measured in a samesection. Therefore, these four measurement items may correspond to threemeasurement sections: the head circumference section (HC and BPD maycorrespond to a same section), the abdominal circumference section andthe femur section. The head circumference section contains the skull ofthe fetus, which is represented as a high intensity portion in anultrasound section image. Furthermore, the skull in the near field andthe skull in the far field together form an ellipse, in which the brainof the fetus is located. The gray values within the ellipse aresignificantly lower than the gray values of the skull. In the femursection, the femur is also represented as a high intensity portion, butthe femur is substantially linear and only has a few curved portions. Inthe abdominal circumference section, the abdominal circumference isrepresented as an elliptic object with great gradient. However, theboundary of the abdominal circumference is not represented as a highintensity portion, and the internal portion and the boundary of theabdominal circumference are close in gray value. Therefore, when theimage processing methods are adopted to identify the measurement items,the measuring mode selected by the user may be acquired first, and thenthe image processing methods may be used to identify the measurementitems.

Referring to FIG. 7 , a flow chart of the identification process of thepresent embodiment is shown. The methods of the present embodiment willbe further described hereinafter taking the obstetric measuring mode asan example. The methods may include the following steps.

Step 600: acquiring the measuring mode.

Step 610: extracting the high intensity portion. In this step, the highintensity portion in the specified section image may be extracted basedon the gray values of the pixels of the specified section image.

In an embodiment, after the gray data of the specified section image isacquired, a preprocessing may be performed on the image and then thehigh intensity portion may be extracted based on the preprocessed image.The preprocessing may be mainly used to suppress the noise of the image,increase the continuity of the boundary and highlight the high intensityportion. There are many preprocessing methods, such as anisotropicsmoothing, etc. In other embodiments, the preprocessing may not beperformed and the high intensity portion may be extracted based on theoriginal gray data of the image.

In an embodiment, a cluster segmentation method may be used to extractthe high intensity portion in the specified section image. The grayvalues of the pixels of the specified section image may be categorizedinto a plurality of categories using the cluster segmentation method.The pixels with the gray values of the one or more categories withmaximum gray value may be kept unchanged while the pixels with the grayvalues of other categories may be assigned with zero, thereby obtaininga characteristic image. For example, the gray values of the specifiedsection image may be categorized into N categories (for example, N=3).The N categories may be arranged in descending order, and the first Mcategories (for example, M≥1 and M<N) may be determined as the categorywith maximum intensity (i.e., the high intensity portion). The pixelswith the gray values of the following N-M categories may be assignedwith zero while other pixels may be kept unchanged, thereby obtainingthe characteristic image. Connected regions in the characteristic imagemay be identified, and one or more connected regions with maximumintensity may be determined, thereby obtaining the high intensityportion of the specified section image. In an embodiment, the first Xconnected regions of the connected regions arranged in descending ordermay be determined as the connected regions with maximum intensity. X maybe preset by the apparatus, and generally may be 1˜10. In otherembodiments, X may be another value based on actual needs.

In an embodiment, the characteristic image may also be obtained byconvoluting a preset M×N operator with the specified section image. Thevalues of the elements in the operator may be determined based on actualneeds.

Furthermore, the characteristic image may generally contain a lot ofnoise. Therefore, certain morphological operations (such asmorphological corrosion, opening operation, removal of small areas orthe like) may be performed on the characteristic image to remove thenoise and increase the continuity of the boundaries in the image.

Step 620: identifying the type of the section. In this step, anidentification method may be performed on the high intensity portionbased on the measuring mode to determine the section type of thespecified section image.

In obstetric measuring mode, both the head circumference and theabdominal circumference are substantially elliptic. The headcircumference section (as shown in FIG. 8 a ) and the abdominalcircumference section (as shown in FIG. 8 b ) mainly differ as follows.The head circumference section contains the skull 81, which isrepresented as high intensity echoes during ultrasound examination.Other tissues of the head, which are represented as low intensityportions during the ultrasound examination and are significantlydifferent from the skull 81 in intensity, are located within the headcircumference. Meanwhile, the abdominal circumference section containsthe abdomen 82 of the fetus, which has a boundary of lower intensitythan the head circumference, and within which the echoes are relativelyuniform and not much different from those of the boundary. Both the headcircumference and the femur section (as shown in FIG. 8 c ) containbones with high intensity, of which the main difference is that the headcircumference contains two symmetrical, arc-shaped bones of skull 81while the femur section contains generally only one, relative straightbone 83. Based on the physical facts above, in this step, the intensityand shape of the connected regions identified may be analyzed andcompared to determine which type of section the section image belong to.

In an embodiment, in a known measuring mode (for example, the obstetricmeasuring mode), an intensity threshold for the connected regions may beset in order to determine whether bones are contained in the sectionimage. For example, in the case where the mean intensity of theconnected regions is greater than a first threshold, the specifiedsection image contains bones; in the case where the mean intensity ofthe connected regions is lower than a second threshold, the specifiedsection image does not contain bones. The first threshold is greaterthan the second threshold. In other embodiments, it is also possiblethat only one intensity threshold for the connected regions is set.

A simple method for the analysis of the shape of the bones is usingcurvature to determine whether the bone bends and, if so, the degree ofthe bending. For example, in a known measuring mode (for example, theobstetric measuring mode), curvature thresholds may be set. In the casewhere the curvature of the connected regions is greater than a curvaturethreshold, the specified section image contains curved bone; in the casewhere the curvature of the connected regions is lower than a curvaturethreshold, the specified section image does not contain curved bone. Inone embodiment, the curvature may be defined as the angle between twolines formed by respectively connecting the point located at the middleof the connected region and the points located at the both ends of theconnected region. In other embodiments, the curvature may be defined inother ways as long as it can represent the degree of the bending of theconnected regions.

For example, in the obstetric measuring mode, in the case where the meanintensities of the X connected regions are greater than a certainthreshold, it is determined that the specified section image containsbones and the section may be the head circumference section or the femursection. Then, the curvature of the connected regions may be considered.In the case where the curvatures are greater than a certain threshold,it is determined that the bones contained in the section are curved andthe specified section is the head circumference section. In the casewhere the curvatures are lower than a certain threshold, it isdetermined that the bones contained in the section are straight and thespecified section is the femur section. In the case where the meanintensities of the X connected regions are all lower than a certainthreshold, it is determined that the specified section does not containbones with high intensity and is an abdominal section.

Step 630: determining the measurement items. In this step, themeasurement items corresponding to the specified section image may bedetermined based on the section type determined and the measurementitems corresponding thereto.

A person skilled in the art will understand that all or parts of thesteps of the methods of the embodiments described above may beimplemented by instructing related hardware using programs. The programsmay be stored in a computer-readable storage medium which may includeROM, RAM, disk or CD, etc.

The present disclosure is described above with reference to specificembodiments. However, it is only used to facilitate the understandingof, but not limit, the present disclosure. Variations to the specificembodiments described above may be made by a person skilled in the artbased on the concepts of the present disclosure.

What is claimed is:
 1. A method for automatic identification of ameasurement item, comprising: acquiring gray values of pixels of aspecified section image, wherein the gray values of the pixelscorrespond to ultrasound echoes generated by reflection of ultrasoundwaves by a tissue under examination; automatically determining a sectiontype of the specified section image based on one or more characteristicsdefined by the gray values of the pixels, the section type identifying aparticular section of a particular area of the tissue from which thespecified section image was acquired, wherein the determined sectiontype is a head circumference section which contains a skull of a fetus,an abdominal circumference section which contains an abdomen of a fetusor a femur section which contains a thigh bone of a fetus; obtaining acorrespondence between the section type and one or more measurementitems which are measurable in an image of the section type;automatically identifying at least one measurement item which ismeasurable in the specified section image according to thecorrespondence and the section type of the specified section image; andobtaining a value of the identified at least one measurement itemaccording to the specified section image.
 2. The method of claim 1,wherein the measurement item is identified based on a comparativeanalysis of the gray values of the pixels with a preset data model. 3.The method of claim 2, further comprising acquiring a measuring modeused during tissue examination.
 4. The method of claim 1, whereinautomatically determining the section type of the specified sectionimage based on the one or more characteristics defined by the grayvalues of the pixels comprises: generating a characteristic of thespecified section image based on the gray values of the pixels of thespecified section image; comparing the characteristic of the specifiedsection image with characteristics of training samples in a presettraining sample model, respectively; and searching a training samplewhose characteristic is most similar to the characteristic of thespecified section image and determining a section type of the trainingsample searched out as the section type of the specified section image.5. The method of claim 3, wherein automatically determining the sectiontype of the specified section image based on the one or morecharacteristics defined by the gray values of the pixels comprises:extracting a high intensity portion from the specified section imagebased on the gray values of the pixels of the specified section image;and performing an identification on the high intensity portion based onthe measuring mode to determine the section type of the specifiedsection image.
 6. The method of claim 1, wherein obtaining the value ofthe identified at least one measurement item comprises obtaining thevalue of the identified at least one measurement item manually,semi-automatically or automatically.
 7. The method of claim 1, whereinthe particular section of the section type corresponds to a directionalong which a desired section image is acquired from the particular areaof the tissue.
 8. The method of claim 1, wherein automaticallydetermining the section type of the specified section image based on theone or more characteristics defined by the gray values of the pixelscomprises: extracting section type characteristics of the specifiedsection image based on which one section type is distinguished withanother section type; and determining the section type of the specifiedsection imaged based on the extracted section type characteristics. 9.The method of claim 1, wherein the one or more characteristics definedby the gray values of the pixels are a shape, a brightness range or asize define by the gray values of the pixels.
 10. A method for automaticidentification of a measurement item, comprising: acquiring gray valuesof pixels of a specified section image, wherein the gray values of thepixels correspond to ultrasound echoes generated by reflection ofultrasound waves by a tissue under examination; automaticallyidentifying at least one measurement item which is measurable in thespecified section image; determining an object corresponding to theidentified at least one measurement item in the specified section image,wherein determining the object corresponding to the identified at leastone measurement item in the specified section image comprises:automatically determining the object corresponding to the identified atleast one measurement item in the specified section image; or detectinga trace operation of an operator on the specified section image, anddetermining the object corresponding to the identified at least onemeasurement item in the specified section image according to thedetected trace operation; and obtaining a value of the identified atleast one measurement item by performing a measurement on the determinedobject.
 11. The method of claim 10, wherein automatically identifyingthe at least one measurement item which is measurable in the specifiedsection image comprises: generating a characteristic of the specifiedsection image based on the gray values of the pixels of the specifiedsection image; comparing the characteristic of the specified sectionimage with characteristics of training samples in a preset trainingsample model, respectively; and searching a training sample whosecharacteristic is most similar to the characteristic of the specifiedsection image and outputting a measurement item corresponding to thetraining sample searched out as the at least one measurement item whichis measurable in the specified section image.
 12. The method of claim11, wherein the characteristic of the specified section image comprisesan eigenvalue or an eigenvector calculated based on the values of thepixels.
 13. The method of claim 10, wherein obtaining the value of theidentified at least one measurement item comprises obtaining the valueof the identified at least one measurement item manually,semi-automatically or automatically.
 14. A method for automaticidentification of a measurement item, comprising: acquiring gray valuesof pixels of a specified section image, wherein the gray values of thepixels correspond to ultrasound echoes generated by reflection ofultrasound waves by a tissue under examination; automaticallyidentifying at least one measurement item which is measurable in thespecified section image, wherein automatically identifying the at leastone measurement item which is measurable in the specified section imagecomprises: generating a characteristic of the specified section imagebased on the gray values of the pixels of the specified section image,wherein the characteristic of the specified section image comprises aneigenvalue or an eigenvector calculated based on the gray values of thepixels; comparing the characteristic of the specified section image withcharacteristics of training samples in a preset training sample model,respectively; and searching a training sample whose characteristic ismost similar to the characteristic of the specified section image andoutputting a measurement item corresponding to the training samplesearched out as the at least one measurement item which is measurable inthe specified section image; determining an object corresponding to theidentified at least one measurement item in the specified section image;and obtaining a value of the identified at least one measurement item byperforming a measurement on the determined object.